ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2024
BioinspiredLLM: Conversational Large Language Model for the Mechanics of Biological and Bio-Inspired Materials.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Autogenerating a Domain-Specific Question-Answering Data Set to Enable High-Performing Language Models for Magnetic Materials.Journal of chemical information and modeling · 2026Article
- From Biological Models to Industrial Production: How Industry 4.0 Enables the Manufacturability and Scalability of Biomimetic Design.Biomimetics (Basel, Switzerland) · 2026Review
- A pretrained biomedical large language model for Persian biomedical text mining.Scientific reports · 2026Article
- The application of large language models in orthopedic postgraduate education: potentials, challenges, and future prospects.Journal of orthopaedic surgery and research · 2026Review
- Sustainable Materials Design With Multi-Modal Artificial Intelligence.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Prompt Engineering Accelerates the Data-Driven Discovery of Photocatalysts via an LLM-Based Model Ensemble Strategy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Coastal Zone Information Model: A comprehensive architecture for coastal digital twin by integrating data, models, and knowledge.Fundamental research · 2026Review
- The Advanced BioTRIZ Method Based on LTE and MPV.Biomimetics (Basel, Switzerland) · 2026Article
- Graph retrieval augmented large language models for facial phenotype associated rare genetic disease.NPJ digital medicine · 2025Article
- The Development Landscape of Large Language Models for Biomedical Applications.Annual review of biomedical data science · 2025Review
- The Structure-Mechanics Relationship of Bamboo-Epidermis and Inspired Composite Design by Artificial Intelligence.Advanced materials (Deerfield Beach, Fla.) · 2025Article
- SciAgents: Automating Scientific Discovery Through Bioinspired Multi-Agent Intelligent Graph Reasoning.Advanced materials (Deerfield Beach, Fla.) · 2025Article
- Autonomous Self-Evolving Research on Biomedical Data: The DREAM Paradigm.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Native Silk Fibers: Protein Sequence and Structure Influences on Thermal and Mechanical Properties.Biomacromolecules · 2025Review
- 3D Printable Biocomposites with Tunable Environmental Degradability.3D printing and additive manufacturing · 2025Article
- Learning the rules of peptide self-assembly through data mining with large language models.Science advances · 2025Article
- Automating alloy design and discovery with physics-aware multimodal multiagent AI.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Tuning load redistribution and damage near heterogeneous interfaces.Scientific reports · 2024Article
- Modifying Naturally Occurring, Nonmammalian-Sourced Biopolymers for Biomedical Applications.ACS biomaterials science & engineering · 2024Review
- Generative Retrieval-Augmented Ontologic Graph and Multiagent Strategies for Interpretive Large Language Model-Based Materials Design.ACS engineering Au · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
The study of biological materials and bio-inspired materials science is well established; however, surprisingly little knowledge is systematically translated to engineering solutions. To accelerate discovery and guide insights, an open-source autoregressive transformer large language model (LLM), BioinspiredLLM, is reported. The model is finetuned with a corpus of over a thousand peer-reviewed articles in the field of structural biological and bio-inspired materials and can be prompted to recall information, assist with research tasks, and function as an engine for creativity. The model has proven that it is able to accurately recall information about biological materials and is further strengthened with enhanced reasoning ability, as well as with Retrieval-Augmented Generation (RAG) to incorporate new data during generation that can also help to traceback sources, update the knowledge base, and connect knowledge domains. BioinspiredLLM also has shown to develop sound hypotheses regarding biological materials design and remarkably so for materials that have never been explicitly studied before. Lastly, the model shows impressive promise in collaborating with other generative artificial intelligence models in a workflow that can reshape the traditional materials design process. This collaborative generative artificial intelligence method can stimulate and enhance bio-inspired materials design workflows. Biological materials are at a critical intersection of multiple scientific fields and models like BioinspiredLLM help to connect knowledge domains.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.